Behavior model of an intelligent agent based on a genetic algorithm

A solution of improving the behavior model of a non-player character as an intelligent agent by optimizing input parameters based on a genetic algorithm is presented. The proposed approach includes the development of a non-player character model: a skeleton, rigid bodies, the implementation of a dynamic model based on the Featherstone algorithm, and modeling of the character's behavior based on a genetic algorithm. The formation of a behavior model using a genetic algorithm that simulates the physical properties of a character, taking into account his actions, is proposed. The stages of the genetic algorithm include creating an initial population, fitness score, selection, crossing and mutation. Based on the results of the experiments, the input parameters of the non-player character behavior model were determined, maximizing the cumulative fitness score, which acts as an estimate of the reward, which can be used as initial values for further experiments. Keywords: non-player character, intelligent agent, simulation, genetic algorithm

Download Full-text

Intelligent Agent Feasibility Study. Volume 1: Agent-based System Technology

10.21236/ada359612 ◽

1998 ◽

Author(s):

Stephen E. Deutsch

Keyword(s):

Feasibility Study ◽

Intelligent Agent ◽

System Technology ◽

Agent Based

Download Full-text

Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

Mathematics ◽

10.3390/math9060681 ◽

2021 ◽

Vol 9 (6) ◽

pp. 681

Author(s):

László Barna Iantovics

Keyword(s):

Problem Solving ◽

Scientific Literature ◽

Real Life ◽

Intelligent Agent ◽

Difficult Problem ◽

Black Box ◽

Machine Intelligence ◽

Agent Based ◽

Np Hard Problem ◽

Box Method

Current machine intelligence metrics rely on a different philosophy, hindering their effective comparison. There is no standardization of what is machine intelligence and what should be measured to quantify it. In this study, we investigate the measurement of intelligence from the viewpoint of real-life difficult-problem-solving abilities, and we highlight the importance of being able to make accurate and robust comparisons between multiple cooperative multiagent systems (CMASs) using a novel metric. A recent metric presented in the scientific literature, called MetrIntPair, is capable of comparing the intelligence of only two CMASs at an application. In this paper, we propose a generalization of that metric called MetrIntPairII. MetrIntPairII is based on pairwise problem-solving intelligence comparisons (for the same problem, the problem-solving intelligence of the studied CMASs is evaluated experimentally in pairs). The pairwise intelligence comparison is proposed to decrease the necessary number of experimental intelligence measurements. MetrIntPairII has the same properties as MetrIntPair, with the main advantage that it can be applied to any number of CMASs conserving the accuracy of the comparison, while it exhibits enhanced robustness. An important property of the proposed metric is the universality, as it can be applied as a black-box method to intelligent agent-based systems (IABSs) generally, not depending on the aspect of IABS architecture. To demonstrate the effectiveness of the MetrIntPairII metric, we provide a representative experimental study, comparing the intelligence of several CMASs composed of agents specialized in solving an NP-hard problem.

Download Full-text

A distributed intelligent agent based intrusion detection system using deep learning algorithms

Proceedings of the 2nd International Conference on Digital Tools & Uses Congress ◽

10.1145/3423603.3424054 ◽

2020 ◽

Author(s):

Sami Mezghani ◽

Farah Barika Ktata

Keyword(s):

Deep Learning ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Learning Algorithms ◽

Intelligent Agent ◽

Agent Based

Download Full-text

An ontology, intelligent agent-based framework for the provision of semantic web services

Expert Systems with Applications ◽

10.1016/j.eswa.2008.01.037 ◽

2009 ◽

Vol 36 (2) ◽

pp. 3167-3187 ◽

Cited By ~ 61

Author(s):

Francisco García-Sánchez ◽

Rafael Valencia-García ◽

Rodrigo Martínez-Béjar ◽

Jesualdo T. Fernández-Breis

Keyword(s):

Web Services ◽

Semantic Web ◽

Intelligent Agent ◽

Semantic Web Services ◽

Agent Based

Download Full-text

A GENETIC APPROACH FOR LONG TERM VIRTUAL ORGANIZATION DISTRIBUTION

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213011000152 ◽

2011 ◽

Vol 20 (02) ◽

pp. 271-295 ◽

Cited By ~ 2

Author(s):

VÍCTOR SÁNCHEZ-ANGUIX ◽

SOLEDAD VALERO ◽

ANA GARCÍA-FORNES

Keyword(s):

Genetic Algorithm ◽

Virtual Organization ◽

Genetic Material ◽

Multi Agent System ◽

Crossover Operator ◽

Agent Based ◽

Multi Agent ◽

Global Goal ◽

Computational Resources

An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios.

Download Full-text